Transcription of Ottoman Machine-Print Documents

Abstract

With the ever increasing speed of the digitization process, a large collection of Ottoman documents is accessible to researchers and the general public. But, the majority of the users interested in these documents can not read these documents unless they are transcripted to the modern Turkish script which use an extended version of the Latin alphabet. Manual transcription of such a massive amount of documents is beyond the capacity of human experts. As a solution, we propose an automatic recognition system for printed Ottoman documents which transcribes Ottoman texts directly to the modern Turkish script. We evaluated three decoding strategies including the Word Beam Search decoder that allows to use a recognition lexicon and n-gram statistics during the decoding phase. The system achieves 2.25% character error rate and 6.42% word error rate on a test set of 1.4K samples, using the test set transcriptions as the recognition lexicon. Using a general purpose, large lexicon of the Ottoman era (260K words and 77% test coverage), the performance is measured as 3.68% character error rate and 16.61% word error rate.

A Deep Learning Based Offline Optical Character Recognition Model for Printed Ottoman Turkish

“…In testing, the system achieved a 3.68% CER and a 16.61% Word Error Rate (WER) on a 1.4K line image test set, employing the Word Beam Search (WBS) decoder along with a 260K-word lexicon. The study demonstrated that using a recognition lexicon to restrict transcription output improves accuracy, but this approach is not optimal for the agglutinative nature of the Turkish language [12].…”